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      Online misinformation and vaccine hesitancy

      discussion
      1 , 2 , 3
      Translational Behavioral Medicine
      Oxford University Press
      COVID-19, Media, Misinformation, Vaccine, Vaccine hesitancy

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          Lay Summary

          Vaccine hesitancy, the rejection or delay to get vaccinated even if there is an effective vaccine available, may be instrumental in the resurgence of vaccine-preventable disease. Studies have shown that the rise in nonmedical exemptions for vaccination increases rates of childhood vaccine-preventable disease. One factor that influences vaccine hesitancy is online misinformation. False or misleading information online regarding vaccines can be found in independent news outlets, websites, and social media. The spread of vaccine misinformation is especially important during the COVID-19 pandemic as false information can decrease pro-vaccine opinions. The recent announcement of an effective COVID-19 vaccine became a hot topic online, with many adults hesitant to take the vaccine. Public health experts, medical professionals, and pro-vaccine individuals can help curb the spread of misinformation by correcting false statements online. Social media companies can also aid in stopping misinformation by implementing and enforcing policy that limits misinformation on their platforms.

          Abstract

          Although rates of vaccination have increased worldwide, the rise in nonmedical exemptions for vaccination may have caused a resurgence of childhood vaccine-preventable diseases. Vaccine hesitancy plays an important role in the decreasing rates of vaccination and is considered by the World Health Organization as a top ten global threat to public health. Online vaccine misinformation is present in news outlets, websites, and social media, and its rapid and extensive dissemination is aided by artificial intelligence (AI). In combating online misinformation, public health experts, the medical community, and lay vaccination advocates can correct false statements using language that appeal to those who are undecided about vaccination. As the gatekeepers to online information, they can implement and enforce policy that limits or bans vaccine misinformation on their platforms. AI tools might also be used to address misinformation, but more research is needed before implementing this approach more broadly in health policy. This commentary examines the role that different online platforms appear to be playing in the spread of misinformation about vaccines. We also discuss the implications of online misinformation on attitudes about COVID-19 vaccine uptake and provide suggestions for ways to combat online misinformation.

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          Most cited references35

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          Is Open Access

          Vaccine hesitancy: Definition, scope and determinants.

          The SAGE Working Group on Vaccine Hesitancy concluded that vaccine hesitancy refers to delay in acceptance or refusal of vaccination despite availability of vaccination services. Vaccine hesitancy is complex and context specific, varying across time, place and vaccines. It is influenced by factors such as complacency, convenience and confidence. The Working Group retained the term 'vaccine' rather than 'vaccination' hesitancy, although the latter more correctly implies the broader range of immunization concerns, as vaccine hesitancy is the more commonly used term. While high levels of hesitancy lead to low vaccine demand, low levels of hesitancy do not necessarily mean high vaccine demand. The Vaccine Hesitancy Determinants Matrix displays the factors influencing the behavioral decision to accept, delay or reject some or all vaccines under three categories: contextual, individual and group, and vaccine/vaccination-specific influences.
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            Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA

            Widespread acceptance of a vaccine for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will be the next major step in fighting the coronavirus disease 2019 (COVID-19) pandemic, but achieving high uptake will be a challenge and may be impeded by online misinformation. To inform successful vaccination campaigns, we conducted a randomized controlled trial in the UK and the USA to quantify how exposure to online misinformation around COVID-19 vaccines affects intent to vaccinate to protect oneself or others. Here we show that in both countries-as of September 2020-fewer people would 'definitely' take a vaccine than is likely required for herd immunity, and that, relative to factual information, recent misinformation induced a decline in intent of 6.2 percentage points (95th percentile interval 3.9 to 8.5) in the UK and 6.4 percentage points (95th percentile interval 4.0 to 8.8) in the USA among those who stated that they would definitely accept a vaccine. We also find that some sociodemographic groups are differentially impacted by exposure to misinformation. Finally, we show that scientific-sounding misinformation is more strongly associated with declines in vaccination intent.
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              Deep learning for healthcare: review, opportunities and challenges.

              Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.
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                Author and article information

                Journal
                Transl Behav Med
                Transl Behav Med
                tbm
                Translational Behavioral Medicine
                Oxford University Press (US )
                1869-6716
                1613-9860
                16 September 2021
                16 September 2021
                : ibab128
                Affiliations
                [1 ]ElevateU , Irvine, CA, USA
                [2 ]Department of Emergency Medicine, University of California , Irvine, CA 92697, USA
                [3 ]University of California Institute for Prediction Technology, Department of Informatics, University of California , Irvine, CA, USA
                Author notes
                Correspondence to: SD Young, syoung5@ 123456hs.uci.edu
                Article
                ibab128
                10.1093/tbm/ibab128
                8515268
                34529080
                7163aecb-ee2b-47e2-a481-72ae00b747e0
                © Society of Behavioral Medicine 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

                This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                Page count
                Pages: 6
                Funding
                Funded by: National Institute of Mental Health, DOI 10.13039/100000025;
                Award ID: MH106415
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Categories
                Commentary/Position Paper
                AcademicSubjects/MED00860
                AcademicSubjects/SCI02170
                Custom metadata
                PAP

                Neurology
                covid-19,media,misinformation,vaccine,vaccine hesitancy
                Neurology
                covid-19, media, misinformation, vaccine, vaccine hesitancy

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